# PowerAnalyses.jl

Statistical power analyses in Julia

## Installation

```
using Pkg
Pkg.add("PowerAnalyses")
```

## Introduction

Statistical power is the probability that a test will correctly indicate an effect when there is one.
In other words, it is the inverse of making a Type II error (false negative) β: `power = 1 - β`

.

The priorities of this package are as follows:

- make it easy for anyone to run a power analysis; even for people who never used the Julia programming language before and
- don't overuse Unicode symbols (it is unreasonable to expect that everyone can easily type Unicode)

## Validity

For each test in this package, the result provided by this package is verified by comparing it to either `G*Power`

or `pwr`

see `test/runtests.jl`

for details.

## Usage

The package defines `get_alpha`

, `get_power`

, `get_es`

and `get_n`

.
For example, to get the required sample size `n`

for an effect size `es`

of 0.5, `power`

of 0.95 and significance level `alpha`

of 0.05 for a one sample *t*-test use:

```
julia> using PowerAnalyses
julia> es = 0.5
0.5
julia> alpha = 0.05
0.05
julia> power = 0.95
0.95
julia> n = get_n(OneSampleTTest(two_tails); alpha, power, es)
53.941
```

This number is the same as what you would get via G*Power.

For fun. We can now try to get the original alpha back by passing `n`

to `get_alpha`

:

```
julia> get_alpha(OneSampleTTest(two_tails); power, n, es)
0.049999999999997824
```

Close enough.

See https://huijzer.xyz/PowerAnalyses.jl/ for more information.